How to train a Support Vector Machine(svm) classifier with openCV with facial features?

Question

I want to use the svm classifier for facial expression detection. I know opencv has a svm api, but I have no clue what should be the input to train the classifier. I have read m

Answer 1

the machine learning algos in opencv all come with a similar interface. to train it, you pass a NxM Mat offeatures (N rows, each feature one row with length M) and a Nx1 Mat with the class-labels. like this:

//traindata      //trainlabels

f e a t u r e    1 
f e a t u r e    -1
f e a t u r e    1
f e a t u r e    1
f e a t u r e    -1

for the prediction, you fill a Mat with 1 row in the same way, and it will return the predicted label

so, let's say, your 16 facial points are stored in a vector, you would do like:

Mat trainData; // start empty
Mat labels;

for all facial_point_vecs:
{
    for( size_t i=0; i<16; i++ )
    {
        trainData.push_back(point[i]);
    }
    labels.push_back(label); // 1 or -1
}
// now here comes the magic:
// reshape it, so it has N rows, each being a flat float, x,y,x,y,x,y,x,y... 32 element array
trainData = trainData.reshape(1, 16*2); // numpoints*2 for x,y

// we have to convert to float:
trainData.convertTo(trainData,CV_32F);

SVM svm; // params omitted for simplicity (but that's where the *real* work starts..)
svm.train( trainData, labels );


//later predict:
vector points;
Mat testData = Mat(points).reshape(1,32); // flattened to 1 row
testData.convertTo(testData ,CV_32F);
float p = svm.predict( testData );